Results 51 to 60 of about 305,718 (72)
A Review, Framework and R toolkit for Exploring, Evaluating, and Comparing Visualizations [PDF]
arXiv, 2019 This paper gives a review and synthesis of methods of evaluating dimensionality reduction techniques. Particular attention is paid to rank-order neighborhood evaluation metrics. A framework is created for exploring dimensionality reduction quality through visualization. An associated toolkit is implemented in R. The toolkit includes scatter plots, heat
arxiv
Nonlinear Dimensionality Reduction Based on HSIC Maximization
IEEE Access, 2018 Hilbert-Schmidt independence criterion (HSIC) is typically used to measure the statistical dependence between two sets of data. HSIC first transforms these two sets of data into two reproducing Kernel Hilbert spaces (RKHS), respectively, and then ...
Zhengming Ma +3 more
doaj +1 more source
Gauge field corrections to 11-dimensional supergravity via dimensional reduction [PDF]
, 2017 Using the fact that eleven-dimensional supergravity yields type IIA supergravity under dimensional reduction on a circle, we determine higher-derivative terms of 11-dimensional supergravity including the $ R^4 $, $ ({\partial {F_4}})^2 R^2 $ and $ ({\partial {F_4}})^4 $ terms.
arxiv +1 more source
Dimensionality Reduction in Gene Expression Data Sets
IEEE Access, 2019 Dimensionality reduction is used in microarray data analysis to enhance prediction quality, reduce computing time, and construct more robust models.
Jovani Taveira De Souza +2 more
doaj +1 more source
A Visual Interaction Framework for Dimensionality Reduction Based Data Exploration [PDF]
arXiv, 2018 Dimensionality reduction is a common method for analyzing and visualizing high-dimensional data. However, reasoning dynamically about the results of a dimensionality reduction is difficult. Dimensionality-reduction algorithms use complex optimizations to reduce the number of dimensions of a dataset, but these new dimensions often lack a clear relation ...
arxiv
Dimensionality Reduction Algorithms on High Dimensional Datasets
Emitter: International Journal of Engineering Technology, 2014 Classification problem especially for high dimensional datasets have attracted many researchers in order to find efficient approaches to address them. However, the classification problem has become very complicatedespecially when the number of possible ...
Iwan Syarif
doaj +3 more sources
Dimensionality Reduction via Multiple Locality-Constrained Graph Optimization
IEEE Access, 2018 In recent years, graph-based dimensionality reduction methods became increasingly more significant since they have been successfully applied in various computer vision and machine learning problems.
Caixia Zheng +6 more
doaj +1 more source
Dimensionality Reduction by Weighted Connections between Neighborhoods
Abstract and Applied Analysis, 2014 Dimensionality reduction is the transformation of high-dimensional data into a meaningful representation of reduced dimensionality. This paper introduces a dimensionality reduction technique by weighted connections between neighborhoods to improve K ...
Fuding Xie, Yutao Fan, Ming Zhou
doaj +1 more source
Weighted Local Discriminant Preservation Projection Ensemble Algorithm With Embedded Micro-Noise
IEEE Access, 2019 High-dimensional data often cause the “curse of dimensionality” in data processing. Dimensionality reduction can effectively solve the curse of dimensionality and has been widely used in high-dimensional data processing.
Yuchuan Liu +3 more
doaj +1 more source
A Robust Multi-Subject fMRI Analysis Method Using Dimensional Optimization
IEEE Access, 2019 In blind source separation (BSS) for multisubject functional magnetic resonance imaging (fMRI) data, dimensionality reduction is generally performed for multiple times. This leads to the challenge of determining the number of the retained dimensionality,
Yan Zhang +4 more
doaj +1 more source